code: def batch_to_time(value, dilation):
      with tf.compat.v1.name_scope("batch_to_time"):
          shape = tf.shape(input=value)
          prepared = tf.reshape(value, [dilation, -1, shape[2]])
          transposed = tf.transpose(a=prepared, perm=[1, 0, 2])
          return tf.reshape(
              transposed,
              [tf.compat.v1.div(shape[0], dilation), -1, shape[2]],
          )
  
file_path: finetune/finetune/base_models/oscar/featurizer.py
imports: import tensorflow
  from finetune.base_models.gpt.featurizer import dropout, embed, split_heads, merge_heads
  from finetune.util.shapes import shape_list, lengths_from_eos_idx
  from finetune.custom_ops import recursive_agg
  from finetune.base_models.gpt.featurizer import get_pos_values
  from finetune.optimizers.recompute_grads import recompute_grad
  import functools
label_desc: Use image node_id 4 for calling a global function with example usage: batch_to_time(value, dilation) and returns: tf